QPACE 2 and Domain Decomposition on the Intel Xeon Phi

We give an overview of QPACE 2, which is a custom-designed supercomputer based on Intel Xeon Phi processors, developed in a collaboration of Regensburg University and Eurotech. We give some general recommendations for how to write high-performance code for the Xeon Phi and then discuss our implementation of a domain-decomposition-based solver and present a number of benchmarks.Comment: plenary talk at Lattice 2014, to appear in the conference proceedings PoS(LATTICE2014), 15 pages, 9 figure

Knowledge is power: Quantum chemistry on novel computer architectures

In the first chapter of this thesis, a background of fundamental quantum chemistry concepts is provided. Chapter two contains an analysis of the performance and energy efficiency of various modern computer processor architectures while performing computational chemistry calculations. In chapter three, the processor architectural study is expanded to include parallel computational chemistry algorithms executed across multiple-node computer clusters. Chapter four describes a novel computational implementation of the fundamental Hartree-Fock method which significantly reduces computer memory requirements. In chapter five, a case study of quantum chemistry two-electron integral code interoperability is described. The final chapters of this work discuss applications of quantum chemistry. In chapter six, an investigation of the esterification of acetic acid on acid-functionalized silica is presented. In chapter seven, the application of ab initio molecular dynamics to study the photoisomerization and photocyclization of stilbene is discussed. Final concluding remarks are noted in chapter eight

Janus II: a new generation application-driven computer for spin-system simulations

This paper describes the architecture, the development and the implementation of Janus II, a new generation application-driven number cruncher optimized for Monte Carlo simulations of spin systems (mainly spin glasses). This domain of computational physics is a recognized grand challenge of high-performance computing: the resources necessary to study in detail theoretical models that can make contact with experimental data are by far beyond those available using commodity computer systems. On the other hand, several specific features of the associated algorithms suggest that unconventional computer architectures, which can be implemented with available electronics technologies, may lead to order of magnitude increases in performance, reducing to acceptable values on human scales the time needed to carry out simulation campaigns that would take centuries on commercially available machines. Janus II is one such machine, recently developed and commissioned, that builds upon and improves on the successful JANUS machine, which has been used for physics since 2008 and is still in operation today. This paper describes in detail the motivations behind the project, the computational requirements, the architecture and the implementation of this new machine and compares its expected performances with those of currently available commercial systems.Comment: 28 pages, 6 figure

Acceleration of Large-Scale Electronic Structure Simulations with Heterogeneous Parallel Computing

Large-scale electronic structure simulations coupled to an empirical modeling approach are critical as they present a robust way to predict various quantum phenomena in realistically sized nanoscale structures that are hard to be handled with density functional theory. For tight-binding (TB) simulations of electronic structures that normally involve multimillion atomic systems for a direct comparison to experimentally realizable nanoscale materials and devices, we show that graphical processing unit (GPU) devices help in saving computing costs in terms of time and energy consumption. With a short introduction of the major numerical method adopted for TB simulations of electronic structures, this work presents a detailed description for the strategies to drive performance enhancement with GPU devices against traditional clusters of multicore processors. While this work only uses TB electronic structure simulations for benchmark tests, it can be also utilized as a practical guideline to enhance performance of numerical operations that involve large-scale sparse matrices

Using accelerators to speed up scientific and engineering codes: perspectives and problems

Accelerators are quickly emerging as the leading technology to further boost computing performances; their main feature is a massively parallel on-chip architecture. NVIDIA and AMD GPUs and the Intel Xeon-Phi are examples of accelerators available today. Accelerators are power-efficient and deliver up to one order of magnitude more peak performance than traditional CPUs. However, existing codes for traditional CPUs require substantial changes to run efficiently on accelerators, including rewriting with specific programming languages. In this contribution we present our experience in porting large codes to NVIDIA GPU and Intel Xeon-Phi accelerators. Our reference application is a CFD code based on the Lattice Boltzmann (LB) method. The regular structure of LB algorithms makes them suitable for processor architectures with a large degree of parallelism. However, the challenge of exploiting a large fraction of the theoretically available performance is not easy to met. We consider a state-of-the-art two-dimensional LB model based on 37 populations (a D2Q37 model), that accurately reproduces the thermo-hydrodynamics of a 2D-fluid obeying the equation-of-state of a perfect gas. We describe in details how we implement and optimize our LB code for Xeon-Phi and GPUs, and then analyze performances on single- and multi-accelerator systems. We finally compare results with those available on recent traditional multi-core CPUs

Task-based adaptive multiresolution for time-space multi-scale reaction-diffusion systems on multi-core architectures

A new solver featuring time-space adaptation and error control has been recently introduced to tackle the numerical solution of stiff reaction-diffusion systems. Based on operator splitting, finite volume adaptive multiresolution and high order time integrators with specific stability properties for each operator, this strategy yields high computational efficiency for large multidimensional computations on standard architectures such as powerful workstations. However, the data structure of the original implementation, based on trees of pointers, provides limited opportunities for efficiency enhancements, while posing serious challenges in terms of parallel programming and load balancing. The present contribution proposes a new implementation of the whole set of numerical methods including Radau5 and ROCK4, relying on a fully different data structure together with the use of a specific library, TBB, for shared-memory, task-based parallelism with work-stealing. The performance of our implementation is assessed in a series of test-cases of increasing difficulty in two and three dimensions on multi-core and many-core architectures, demonstrating high scalability